ArticleChildren (Basel, Switzerland)2021
Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.
Article in Children (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Early predictors of persistent childhood asthma and allergic rhinitis: A machine learning study using post-hoc explainability.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study.Journal of personalized medicine · 2024Article
- Epigenomic partitioning of a polygenic risk score for asthma reveals distinct genetically driven disease pathways.The European respiratory journal · 2024Article
- Persistent Asthma in Childhood.Children (Basel, Switzerland) · 2022Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.International journal of environmental research and public health · 2021Article
- Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children.Asthma research and practice · 2021Article
- Prediction of Type 2 Diabetes Based on Machine Learning Algorithm.International journal of environmental research and public health · 2021Article
- Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.